Method and system for detecting sulfur in soil from reflected light

ABSTRACT

The present invention relates to a method of detecting soil nutrients or soil nutrients in soil from reflected light, and also includes systems for the measurement, calculation and transmission of data relating to or carrying out that method.

STATEMENT REGARDING GOVERNMENTAL INTEREST

The present invention was made through funding from grant numbers10390042/-0048/-0050 from the US Department of Agriculture (USDA). TheUnited States Government has certain rights in the invention.

FIELD OF THE INVENTION

The present invention relates to a method of detecting nutrients in soilfrom reflected light.

BACKGROUND AND SUMMARY OF THE INVENTION

Conventional methods for soil sampling and analysis for soil variabilityin chemical characteristics are too time-consuming and expensive formulti-seasonal monitoring over large-scale areas.

In many instances it is desirable to be able to detect the presence ofnutrients in soil, particularly phosphorus, which can enrich nearbylakes in phosphorus via surface water run-off, thereby promotingcyanobacteria blooms in lakes. Showing where soils are less enriched innutrients also can be used in precision farming to direct the farmer toadd only the necessary amount of nutrients when fertilizing soils,thereby reducing the cost of fertilizer to the farmer and reducing thenutrient load added to nearby lakes from surface water run-off from farmfields.

It is particularly desirable to be able to detect the presence ofnutrients in soil in a manner that is convenient and provides relativelyimmediate results so that the public, farmers or agriculturalauthorities may be warned or other actions taken to avoid or eliminatecontamination of the assayed soil.

Application of treated sewage sludges (biosolids) to agricultural landhas become a prominent and acceptable method of waste disposal in recentyears. Biosolids are known to improve soil physical characteristics(Epstein et al., 1975; Wei et al., 1985), increase the organic matterand cation exchange capacity and supply the nutrients required for cropgrowth (Sommers, 1977; Singh and Agrawal, 2008). However, the potentialfor excess application of biosolids, resulting in a build up ofnitrogen, phosphorus (Mantovi et al., 2005), zinc, copper, lead (Mantoviet al., 2005; Udom et al., 2004; Nyamangara and Mzezewa, 1999) andcadmium (Bergkvist et al., 2003) in the surface soils of agriculturalfields continues to be an area of concern. Other types of fertilizerscan build up the nutrients in soils to an impractical, potentiallyharmful level. Accumulation of phosphorus at high concentrations is amajor environmental concern, as it affects the water quality of lakesand rivers in the event of runoff (Shober and Sims, 2003).

Hence, there is an increasing need to continuously monitor the extent ofsoil contamination in biosolid-applied fields, and in other types offertilized fields, also. Even though conventional methods of soilsampling and testing are being used for this purpose, they are oftenexpensive, time-consuming and unsuitable for mapping soil contaminationover large areas.

Remote sensing has been used as an alternative method for determiningand mapping the physical and chemical characteristics of the soil. Highresolution aerial imagery was used to map the organic carbon (Chen etal., 2000), clay content (Sullivan et al., 2005), organic matter andBray-1 phosphorus concentration (Varvel et al., 1999) in bare soils.Dematte et al. (2003) reported that chemical variations in soilresulting from fertilizer applications can be detected, based on theintensity of reflectance. Several studies showed the use of spectralreflectance to determine the soil color (Post et al., 2000), texture andparticle size distribution (Chang et al., 2001), soil moisture (Lobelland Asner, 2002), iron oxides (Ji et al., 2002), carbonates (Ben-Dor andBanin, 1990), clay (Ben-Dor and Banin, 1995), organic carbon (Dalal andHenry, 1986; Morra et al., 1991; Reeves et al., 2002) organic matter(Henderson et al., 1992) and soil phosphorus (Bogrekci and Lee, 2005,2007).

As used herein, remote sensing refers to the capability of obtaininginformation about an object without touching it. Sensors which are notin direct contact with the object are generally used to obtain theinformation. In a more limited context, the information obtained byremote sensing is a function of energy emitted by, absorbed by, orreflected from the object.

As used herein, where the remote sensing is conducted from distancesfrom which vegetation may cover portions of the soil surface, vegetationmay be excluded according to a masking formula, such as elimination fromconsideration of any areas that exceed a lower threshold of theNormalized Difference Vegetation Index (NDVI) standard, which is asimple numerical indicator that can be used to analyze remote sensingmeasurements, typically but not necessarily from a space platform, andassess whether the target being observed contains live green vegetationor not. This may be arrived at by using LANDSAT TM bands 3 and 4 in aformula (4−3)/(4+3) to arrive at a value that is broadly between 0.1 and0.3, preferably about 0.2. Regions above this threshold range would beblacked out or otherwise eliminated from the algorithmic calculationsherein to avoid erroneous results. Regions lower than this thresholdrange may be considered to be sufficiently bare soils from whichaccurate measurements and calculated results may be taken.

The addition of soil contaminants as a result of biosolid applicationtends to be concentrated in surface soil samples (Mantovi et al., 2005;Bergkvist et al., 2003; Udom et al., 2004; Nyamangara and Mzezewa,1999).

However, there remains a need for improved methods of remotedetermination of soil nutrients that offer reduced expense, time savingsand availability of mapping soil contamination over large areas.

SUMMARY OF THE INVENTION

The present invention employs remote sensing technology to determine thechemical contents of soils, especially bare soils. The present inventionincludes methods and systems for remote sensing to map chemicalvariability in soils, especially bare soils.

The present invention allows one to detect and determine nutrients fromreflected light. The invention may be used advantageously for anypurpose, such as (1) to determine changes in elemental concentrations ofsoils amended with biosolids; and (2) to use satellite data, such asLANDSAT TM data (and/or similar satellite or remotely obtainedreluctance data), to map these elemental concentrations of the soilswhen they are not substantially covered by vegetation (“bare”). It willbe understood that the present invention may be applied to any surface,such as any planetary surface.

In general terms, the present invention includes a method of determiningthe presence of soil nutrients (or soil nutrients in soil) as well as ameasurement method followed by transmission of data to a remoteprocessing site. The invention also includes sensing, transmittingand/or reporting systems adapted to determine the presence of soilnutrients through remote sensing.

The Methods

The invention includes a method of determining the presence of soilnutrients (or soil nutrients in soil) from light reflected therefrom.The method comprises the steps of: (a) obtaining a measurement ofreflected light from the soil, the measurement comprising a measurementof the respective amount of light in at least two, preferably fourwavelength ranges; and (b) relating the approximate amount of the soilnutrient to the respective amounts of light by applying an algorithmusing a processor relating the respective amounts of light in the atleast two, and preferably four wavelength ranges to the amount of soilnutrients or soil nutrients in the soil.

The processor may be a microprocessor having programming instructionsfor applying the algorithm.

It is preferred that the algorithm comprises a linear relationshipbetween the approximate amount of the soil nutrient in the soil and sumof (a) the ratio of the amount of light in the first wavelength range tothe amount of light in the second wavelength range and (b) the ratio ofthe amount of light in the third wavelength range to the amount of lightin the fourth wavelength range. Typically, wavelength ranges may alsoinclude single wavelengths, so it will be understood that reference towavelength ranges herein also include single wavelengths.

The wavelength ranges typically will be discreet ranges for mostdetectors, such as satellites, although amounts of light in overlappingranges may be used as well.

It is preferred that the values of the reflectance are determined asdark object subtracted values as DOS-corrected digital number (DN)values of the selected spectral bands (i.e., wavelength ranges), such asin the case of satellite spectral bands.

The substances detected and determined herein may also be found whetherthey are considered nutrients or contaminants.

For instance, algorithms based on spectral ratios of LANDSAT TM bands 1,3, 5 and 7 have been developed and correlated with the actualconcentrations of phosphorus, copper and sulfur in soil samplescollected from 70 locations across two fields within 24 hours prior toLANDSAT overpass.

It will be understood that reference to the concentrations ofphosphorus, copper and sulfur in soil samples means the detection ofthese elements and/or each of them in whatever oxidation state or otherbound state they may be present in the target soil or othersubstantially bare substrate (such as in natural or unnatural alluvia,as in the case of watershed run-out or man-made spills or otherdeposits, such as those that may be environmentally damaging.

Typically, the vast majority of phosphorus, sulfur and copper in soilsamples will be present in the form of phosphates, sulfates and cupricsalts, respectively.

The reflectance of the light in each wavelength region varies with thepresence of elemental phosphorus, sulfur and copper, such that the totalconcentration of phosphorus, copper and sulfur in soil samples may bedetermined in accordance with the present invention.

Preferably, the measurement of the amount of light in the at least fourwavelength ranges comprises the measurement, respectively, of: (i)LANDSAT Thematic Mapper (“TM”) band 1, (ii) LANDSAT TM band 3, (iii)LANDSAT TM band 5 and (iv) LANDSAT TM band 7.

Preferred examples of these algorithms are as follows:P(mg/kg)=4156−1690(R51)+2257(R73);Cu(mg/kg)=75−17.9(R51)+21.9(R73);S(mg/kg)=507−14.7(R51)+214(R73),wherein R51 and R73 are the reflectance measurements, preferablydark-object subtracted (haze corrected) values, of TM band 5 divided byTM band 1 and TM band 7 divided by TM band 3, respectively.

It will be understood that the method and system of the presentinvention may be used for the determination/estimation of any or all ofthe soil nutrients phosphorus, copper and/or sulfur in soil samples byusing algorithms to relate their amounts to the reflected light, such asdescribed herein.

The present invention therefore includes a method for determining thepresence of soil nutrients or soil nutrients in soil from lightreflected therefrom, the device comprising (a) a measurement deviceadapted to measure reflected light from the soil, the measurementcomprising a measurement of the respective amount of light in at leastfour wavelength ranges: (i) from about 0.45 μm to about 0.52 μm; (ii)from about 0.63 μm to about 0.69 μm; (iii) from about 1.55 μm to about1.75 μm; and (iv) from about 2.08 μm to about 2.35 μm; and (b) aprocessor at the remote site and capable of relating the approximateamount of the nutrient in the soil to the respective amounts of light byapplying an algorithm using a microprocessor relating the respectiveamounts of light in the at least four wavelength ranges to the amount ofsoil nutrients or soil nutrients in the soil.

These algorithms can provide output of soil nutrient concentrations(e.g. P, Cu and/or S) of bare soils in mg/kg (the same as ppm) whenapplied to LANDSAT TM imagery, which passes overhead every 16 days.There are at least three different uses for the present invention: (1)mapping the nutrient concentrations in the soils of watersheds anddrainage basins of rivers and tributaries; (2) providing farmers withsoil nutrient concentrations of their agricultural fields; and (3)monitoring the environmental disasters involving the spill of toxicchemical contaminants.

This may be expressed in milligrams per kilogram, parts per million orotherwise through appropriate adjustment of the magnitude and dimensionsof the algorithms described herein or generated by the present method.It will be understood that the expression of the amount of soil nutrientin terms of milligrams per kilogram, parts per million are only two ofseveral ways to express the amount, and that reference to mathematicalequivalents refers to any mathematically or logically related algorithmsor expressions.

The method according to the present invention is such that thecalculated value of soil nutrients correlates to the actual measuredamount of the soil nutrients (based upon well-known physical samplingtechniques) by an adjusted square correlation value (i.e., R² adjusted)in excess of 45% and as high as in excess of 65%.

Method of Detecting Phosphorus in Soil from Reflected Light

With respect to phosphorus, the methods of the present invention includea method of measuring phosphorus in soil from light reflected therefrom,the method comprising the steps of: (a) obtaining a measurement ofreflected light from the soil using a light measurement device, themeasurement comprising a measurement of the respective amount of lightin at least two wavelength ranges; and (b) relating the approximateamount of phosphorus in the soil to the respective amounts of light byapplying an algorithm using a microprocessor to relate the respectiveamounts of light in the at least two wavelength ranges to the amount ofphosphorus in the soil.

It is preferred that the algorithm comprises a ratio of the respectiveamount of light in the at least two wavelength ranges.

It is more preferred that the measurement comprises a measurement of therespective amount of light in at least four wavelength ranges, and mostpreferably that the algorithm comprises a ratio of the respective amountof light in a first pair of the four wavelength ranges, and a ratio ofthe respective amount of light in a second pair of the four wavelengthranges.

With respect to phosphorus, the methods of the present invention includea preferred method of measuring phosphorus in soil from light reflectedtherefrom, the method comprising the steps of: (a) obtaining ameasurement of reflected light from the soil, the measurement comprisinga measurement of the respective amount of light in at least fourwavelength ranges: (i) from about 0.45 μm to about 0.52 μm; (ii) fromabout 0.63 μm to about 0.69 μm; (iii) from about 1.55 μm to about 1.75μm; and (iv) from about 2.08 μm to about 2.35 μm from about 2.08 μm toabout 2.35 μm; and (b) relating the approximate amount of phosphorus inthe soil to the respective amounts of light by applying an algorithmusing a microprocessor relating the respective amounts of light in theat least four wavelength ranges to the amount of phosphorus in the soil.

As used herein, reference to mathematical equivalents as applied toalgorithms means any algorithm that achieves substantially the samemathematical result from which the concentration of the target substancemay be derived. Mathematical equivalents thus may also include thosethat vary from those disclosed herein in terms of the coefficients used,with the natural and expected variance in accuracy, which may betolerated in some applications.

The measurement of the amount of light in the at least four wavelengthranges comprises the measurement, respectively, of: (i) LANDSAT TM band1, (ii) LANDSAT TM band 3, (iii) LANDSAT TM band 5 and (iv) LANDSAT TMband 7. It is preferred that, where the nutrient is phosphorus, thealgorithm is selected from the group consisting of P(mg/kg)=K₁−K₂(R51)+K₃ (R73) and mathematical equivalents thereof. In that algorithm,it is preferred that:

-   -   P is the amount of phosphorus expressed in milligrams per        kilogram;    -   K₁ is a value in the range of from about 4000 to about 4300;    -   K₂ is a value in the range of from about 1600 to about 1780;    -   K₃ is a value in the range of from about 2150 to about 2350;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3        and more preferably that:    -   P is the amount of phosphorus expressed in milligrams per        kilogram;    -   K₁ is a value in the range of from about 4100 to about 4200;    -   K₂ is a value in the range of from about 1650 to about 1700;    -   K₃ is a value in the range of from about 2200 to about 2300;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3.        The most preferred values are;    -   P is the amount of phosphorus expressed in milligrams per        kilogram;    -   K₁ is a value of about 4156±3;    -   K₂ is a value of about 1690±3;    -   K₃ is a value of about 2257±3;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3.        Method of Detecting Copper in Soil from Reflected Light

With respect to copper, the methods of the present invention include amethod of measuring copper in soil from light reflected therefrom, themethod comprising the steps of: (a) obtaining a measurement of reflectedlight from the soil using a light measurement device, the measurementcomprising a measurement of the respective amount of light in at leasttwo wavelength ranges; and (b) relating the approximate amount of copperin the soil to the respective amounts of light by applying an algorithmusing a microprocessor to relate the respective amounts of light in theat least two wavelength ranges to the amount of copper in the soil.

It is preferred that the algorithm comprises a ratio of the respectiveamount of light in the at least two wavelength ranges.

It is more preferred that the measurement comprises a measurement of therespective amount of light in at least four wavelength ranges, and mostpreferably that the algorithm comprises a ratio of the respective amountof light in a first pair of the four wavelength ranges, and a ratio ofthe respective amount of light in a second pair of the four wavelengthranges.

With respect to copper, the methods of the present invention include apreferred method of measuring copper in soil from light reflectedtherefrom, the method comprising the steps of: (a) obtaining ameasurement of reflected light from the soil, the measurement comprisinga measurement of the respective amount of light in at least fourwavelength ranges: (i) from about 0.45 μm to about 0.52 μm; (ii) fromabout 0.63 μm to about 0.69 μm; (iii) from about 1.55 μm to about 1.75μm; and (iv) from about 2.08 μm to about 2.35 μm; and (b) relating theapproximate amount of copper in the soil to the respective amounts oflight by applying an algorithm using a microprocessor relating therespective amounts of light in the at least four wavelength ranges tothe amount of copper in the soil.

The measurement of the amount of light in the at least four wavelengthranges comprises the measurement, respectively, of: (i) LANDSAT TM band1, (ii) LANDSAT TM band 3, (iii) LANDSAT TM band 5 and (iv) LANDSAT TMband 7.

It is preferred that, where the nutrient is copper, the algorithm isselected from the group consisting of Cu (mg/kg)=K₁−K₂ (R51)+K₃ (R73)and mathematical equivalents thereof, wherein:

-   -   Cu is the amount of copper expressed in milligrams per kilogram;    -   K₁ is a value in the range of from about 60 to about 90;    -   K₂ is a value in the range of from about 16 to about 20;    -   K₃ is a value in the range of from about 20 to about 24;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3,        and preferably wherein:    -   Cu is the amount of copper expressed in milligrams per kilogram;    -   K₁ is a value in the range of from about 70 to about 80;    -   K₂ is a value in the range of from about 17 to about 19;    -   K₃ is a value in the range of from about 21 to about 23;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3.        In this algorithm, it is most preferred that:    -   Cu is the amount of copper expressed in milligrams per kilogram;    -   K₁ is a value of about 75±3;    -   K₂ is a value of about 17.9±3;    -   K₃ is a value of about 21.9±3;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3.        Method of Detecting Sulfur in Soil from Reflected Light

With respect to sulfur, the methods of the present invention include amethod of measuring sulfur in soil from light reflected therefrom, themethod comprising the steps of: (a) obtaining a measurement of reflectedlight from the soil using a light measurement device, the measurementcomprising a measurement of the respective amount of light in at leasttwo wavelength ranges; and (b) relating the approximate amount of sulfurin the soil to the respective amounts of light by applying an algorithmusing a microprocessor to relate the respective amounts of light in theat least two wavelength ranges to the amount of sulfur in the soil.

It is preferred that the algorithm comprises a ratio of the respectiveamount of light in the at least two wavelength ranges.

It is more preferred that the measurement comprises a measurement of therespective amount of light in at least four wavelength ranges, and mostpreferably that the algorithm comprises a ratio of the respective amountof light in a first pair of the four wavelength ranges, and a ratio ofthe respective amount of light in a second pair of the four wavelengthranges.

With respect to sulfur, the methods of the present invention include apreferred method of measuring sulfur in soil from light reflectedtherefrom, the method comprising the steps of: (a) obtaining ameasurement of reflected light from the soil, the measurement comprisinga measurement of the respective amount of light in at least fourwavelength ranges: (i) from about 0.45 μm to about 0.52 μm; (ii) fromabout 0.63 μm to about 0.69 μm; (iii) from about 1.55 μm to about 1.75μm; and (iv) from about 2.08 μm to about 2.35 μm; and (b) relating theapproximate amount of sulfur in the soil to the respective amounts oflight by applying an algorithm using a microprocessor relating therespective amounts of light in the at least four wavelength ranges tothe amount of sulfur in the soil.

The measurement of the amount of light in the at least four wavelengthranges comprises the measurement, respectively of: (i) LANDSAT TM band1, (ii) LANDSAT TM band 3, (iii) LANDSAT TM band 5 and (iv) LANDSAT TMband 7.

It is preferred that, where the nutrient is sulfur, the algorithm isselected from the group consisting of S (mg/kg)=K₁−K₂ (R51)+K₃ (R73) andmathematical equivalents thereof, wherein:

-   -   S is the amount of sulfur expressed in milligrams per kilogram;    -   K₁ is a value in the range of from about 450 to about 550;    -   K₂ is a value in the range of from about 13 to about 17;    -   K₃ is a value in the range of from about 210 to about 220;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3;        and preferably wherein:    -   S is the amount of sulfur expressed in milligrams per kilogram;    -   K₁ is a value in the range of from about 480 to about 530;    -   K₂ is a value in the range of from about 14 to about 16;    -   K₃ is a value in the range of from about 212 to about 216;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3.        In this algorithm, it is most preferred that:    -   S is the amount of sulfur expressed in milligrams per kilogram;    -   K₁ is a value of about 507±3;    -   K₂ is a value of about 14.7±3;    -   K₃ is a value of about 214±3;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3.

The present invention in all its embodiments may additionally comprisethe step of generating a report of the approximate amount of thenutrient species in the soil. This may be done using electronics adaptedto digitize and process the data using an appropriate algorithm, such asthat described herein. For instance, the report may include an estimateof the amount of the nutrient(s) per kilogram of soil.

The method of the present invention in all its embodiments may alsoinclude the step of transmitting data relating to the approximate amountof the nutrient(s) in the soil to a site remote from the site where themeasurement takes place. This may be done using any transmission methodincluding land line or wireless transmission.

The method of the present invention in all its embodiments mayadditionally include a display of an image representing the datagenerated by the system, so as to be able to visualize the results ofthe assay method carried out by the system, in accordance with thepresent invention.

This method may also be used advantageously where the reflected light issensed remotely by aircraft, satellite, vehicle (such as typically, amotorized piece of farming equipment or the like), or from a pole,building or other fixed support. For instance, the measurement devicemay include sensors adapted to measure the same spectral bands on atractor or other farming vehicle, such as for measuring the phosphorus,sulfur and/or copper in the bare soil, such as by being mounted in frontand/or in back of the tractor.

Processing of the data may take place at the site of light uptake or maybe carried out at a remote location after transmission of the raw data.The estimated report may be sent to farmers, businesses, agriculturalreporting stations, farm bureaus and cooperative offices, or to publicauthorities, to advise or warn of the level of soil nutrients or soilcontaminants, especially where those levels are outside desired levels,or otherwise at elevated, deficient or dangerous levels.

The method of the present invention may further include taking someremediation action or other action to alter the soil nutrients or soilcontaminants, which may be any logical action consistent withenvironmental and/or agricultural practices, such as through the washingor irrigation of soils, application of fertilizer or other soilamendment, or excavation of soils to remove contaminants, etc.

The Systems

The invention also includes a system for determining the presence ofsoil nutrients or soil nutrients in soil from light reflected therefrom,the system comprising: (a) a measurement device adapted to measurereflected light from the soil, the measurement comprising a measurementof the respective amount of light in at least four wavelength ranges,preferably such as those set forth above, and (b) a processor capable ofrelating the approximate amount of the nutrient(s) in the soil to therespective amounts of light by applying an algorithm using amicroprocessor relating the respective amounts of light in the at leastfour wavelength ranges to the amount of soil nutrients or soil nutrientsin the soil.

The invention also includes a system for determining the presence ofsoil nutrients or soil nutrients in soil from light reflected therefrom,the system comprising: (a) electronic means adapted to measure reflectedlight from the soil, the measurement comprising a measurement of therespective amount of light in at least four wavelength ranges,preferably such as those set forth above, and (b) electronic meanscapable of relating the approximate amount of the nutrient(s) in thesoil to the respective amounts of light by applying an algorithm using amicroprocessor relating the respective amounts of light in the at leastfour wavelength ranges to the amount of soil nutrients or soil nutrientsin the soil.

System for Detecting Phosphorus in Soil from Reflected Light

For phosphorus, the system of the present invention may be understood asa system for measuring phosphorus in soil from light reflectedtherefrom, the device comprising: (a) a measurement device adapted tomeasure reflected light from the soil, the measurement comprising ameasurement of the respective amount of light in at least fourwavelength ranges: (i) from about 0.45 μm to about 0.52 μm; (ii) fromabout 0.63 μm to about 0.69 μm; (iii) from about 1.55 μm to about 1.75μm; and (iv) from about 2.08 μm to about 2.35 μm; and (b) a processorcapable of relating the approximate amount of the phosphorus in the soilto the respective amounts of light by applying an algorithm using amicroprocessor relating the respective amounts of light in the at leastfour wavelength ranges to the amount of phosphorus in the soil.

The measurement of the amount of light in the at least four wavelengthranges comprises the measurement, respectively, of: (i) LANDSAT TM band1, (ii) LANDSAT TM band 3, (iii) LANDSAT TM band 5 and (iv) LANDSAT TMband 7.

Where the nutrient is phosphorus, the algorithm may be selected from thegroup consisting of P (mg/kg)=K₁−K₂ (R51)+K₃ (R73) and mathematicalequivalents thereof. It is in such algorithm that:

-   -   P is the amount of phosphorus expressed in milligrams per        kilogram;    -   K₁ is a value in the range of from about 4100 to about 4200;    -   K₂ is a value in the range of from about 1650 to about 1700;    -   K₃ is a value in the range of from about 2200 to about 2300;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3.        and most preferably that:    -   P is the amount of phosphorus expressed in milligrams per        kilogram;    -   K₁ is a value of about 4156±3;    -   K₂ is a value of about 1690±3;    -   K₃ is a value of about 2257±3;    -   R51 is a ratio of the amount of reflected light in LANDSAT TM        band 5 to the amount of reflected light in LANDSAT TM band 1;        and    -   R73 is a ratio of the amount of reflected light in LANDSAT TM        band 7 to the amount of reflected light in LANDSAT TM band 3.

The measurement device and processor may be incorporated into the samearticle or vehicle, or may be distributed between different componentsof the system.

The processor may be of any type appropriate to carry out thecalculation and determination/estimation of the amount of the targetsubstance as described herein. It may be in data communicative contactwith the measurement device through any appropriate means, such asthrough the use of data transmission means and/or storage media knownand used in the information technology and data processing fields.

The measurement device may be selected from the group consisting ofcameras, photosensors and satellites.

The system of the present invention may additionally include a reportgenerator adapted to generate a report of the approximate amount of thenutrient(s) in the soil. Such a report generator may be any device thatis adapted to place the data into a tangible medium, such as a printer,CD burner, flash memory, magnetic storage media, etc.

The system of the present invention may additionally include a displayfor displaying an image representing the data generated by the system,so as to be able to visualize the results of the assay method carriedout by the system, in accordance with the present invention. Typicaldigital images for use in this method may be prepared from digitalinformation taken from aerial platforms or satellites, and either may bestored digitally when taken or transferred into digital format. Typicalsources of data from digital images may include digital or film camerasor spectrometers carried by aircraft or satellite.

The system may additionally include a transmitter adapted to transmitdata relating to the approximate amount of the nutrients in the soilfrom the processor to a site remote from the site where the measurementtakes place. Such a transmitter may include those adapted to send datasuch as through land line or wireless transmission, including telephone,internet, cell phone, radio and the like.

The measurement device may be any device adapted to sense and recordand/or transmit the light frequencies described above. Examples includephotosensors or any appropriate type considering the distances,reflectivity profile, dispersion, and reflectance in each application ofthe invention, cameras, digital cameras and video cameras, etc.

The processor may be any data processing device having programminginstructions for applying the algorithm(s), such as preferably amicroprocessor.

It is preferred that the algorithm comprises a linear relationshipbetween the approximate amount of the nutrient(s) in the soil and sum of(a) the ratio of the first wavelength to the second wavelength and (b)the ratio of the third wavelength to the fourth wavelength.

The measurement device may be placed in any position from which it cansense the required light frequencies, such as on an aircraft orsatellite or on a support, such as a dedicated tower structure, such asa barn or silo. The measurement device may also be in the form of ahandheld device, such as a camera connected to a processor forprocessing the recorded light frequencies, the device may also be in theform of a device similar to a personal digital assistant with lightrecording and processing functions. For instance, the measurement devicemay include sensors adapted to measure the same spectral bands on atractor or other farming vehicle, such as for measuring the phosphorus,sulfur and/or copper in the bare soil, such as by being mounted in frontand/or in back of the tractor.

Another variation of the invention is a system using transmission oflight measurement data to processor at a different location, recognizingthat the processing may be done at a different location than the lightsensing/recording.

In general terms, this variation is a system for determining thepresence of soil nutrients or soil nutrients in soil from lightreflected therefrom, the device comprising (a) a measurement deviceadapted to measure reflected light from the soil, the measurementcomprising a measurement of the respective amount of light in at leastfour wavelength ranges: (i) from about 0.45 μm to about 0.52 μm; (ii)from about 0.63 μm to about 0.69 μm; (iii) from about 1.55 μm to about1.75 μm; and (iv) from about 2.08 μm to about 2.35 μm; and (b) aprocessor at the remote site and capable of relating the approximateamount of the nutrient in the soil to the respective amounts of light byapplying an algorithm using a microprocessor relating the respectiveamounts of light in the at least four wavelength ranges to the amount ofthe soil nutrients in the soil.

Method of Developing Algorithms, Processors and Systems for otherWavelengths and/or Detectors

The invention also includes a method of developing a system fordetermining the presence of soil nutrients or soil nutrients in soilfrom light reflected therefrom, the device comprising (a) obtaining ameasurement of reflected light from the soil, the measurement comprisinga measurement of the respective amount of light of at least twowavelengths or wavelength ranges; (b) developing an algorithm relatingthe respective amounts of light in the at least two wavelengths orwavelength ranges to the amount of soil nutrients or soil nutrients inthe soil through linear regression analysis; (c) producing a processorcapable of relating the approximate amount of the nutrient in the soilto the respective amounts of light by applying an algorithm relating therespective amounts of light in the at least four wavelength ranges tothe amount of soil nutrients or soil nutrients in the soil; and (d)providing a measurement device adapted to measure reflected light fromthe soil and adapted to provide data relating to the measurement to theprocessor.

It is preferred that the method include using a ratio of the respectiveamount of light in each of the at least two wavelengths or wavelengthranges.

It is preferred that the at least two wavelengths or wavelength rangesbe in the visible range (typically wavelengths from about 380 to 750 nm;about between 0.380 and 0.750 micrometers) and/or the infrared range(typically a wavelength between 0.7 and 300 micrometers).

It is more preferred that the method use a measurement of reflectedlight from the soil, the measurement comprising a measurement of therespective amount of light in at least four wavelengths or wavelengthranges.

It is also preferred that the algorithm comprises a ratio of therespective amount of light in said at least two wavelength ranges, andmost preferably that the algorithm comprises a ratio of the respectiveamount of light in a first pair of the four wavelength ranges, and aratio of the respective amount of light in a second pair of the fourwavelength ranges.

The invention also includes a method of developing a system fordetermining the presence of soil nutrients or soil nutrients in soilfrom light reflected therefrom, the device comprising (a) obtaining ameasurement of reflected light from the soil, the measurement comprisinga measurement of the respective amount of light of at least twowavelengths; (b) developing an algorithm relating the respective amountsof light in the at least two wavelengths to the amount of soil nutrientsor soil nutrients in the soil through linear regression analysis; (c)producing a processor capable of relating the approximate amount of thenutrient in the soil to the respective amounts of light by applying analgorithm relating the respective amounts of light in the at least fourwavelength ranges to the amount of soil nutrients or soil nutrients inthe soil; and (d) providing a measurement device adapted to measurereflected light from the soil and adapted to provide data relating tothe measurement to the processor.

The present invention may be extended to other wavelength bands that maybe obtained from other detectors or multiple detectors, such asalternative satellites (or from more than one satellite), such as, forinstance, the Digital Globe World Watch II satellite, which may offerdifferent reflectance bands, and may offer greater resolution thanLANDSAT TM. The method of the present invention may be practiced withany suitable detector from any desired or practical distance, from onlya few feet as in the case of a vehicle-mounted detector, to typicaltower height, to typical aircraft altitudes, to outer space.

Accordingly, the preferred embodiment of the present invention allowsthe operator, for instance, (1) to determine changes in chemicalconcentrations of soils that are amended with treated sewage sludge orrecently treated with fertilizer (which may be compared to recordedfertilizer placement, for agricultural purposes); and 2) to determine ifLANDSAT TM data can be used to map surface chemical characteristics ofsuch amended soils. For this embodiment, two fields in NW Ohio wereselected, designated as F34 and F11 that had been applied with 34 and 11ton acre⁻¹ of biosolids, respectively. Soil samples from a total of 70sampling locations across the two fields were collected one day prior toLANDSAT 5 overpass and were analyzed for several elementalconcentrations. The accumulation of Ba, Cd, Cu, S and P were found to besignificantly higher in the surface soils of field F34, compared tofield F11. Regression equations were established to search foralgorithms that could map these five elemental concentrations in thesurface soils using six, dark-object subtracted (DOS) LANDSAT TM bandsand the 15 non-reciprocal spectral ratios derived from these six bandsfor the May 20, 2005, LANDSAT 5 TM image. Phosphorus (P) had the highestR² adjusted value (67.9%) among all five elements considered, and theresulting algorithm employed only spectral ratios. This model wassuccessfully tested for robustness by applying it to another LANDSAT TMimage obtained on Jun. 5, 2005. The results enabled us to conclude thatLANDSAT TM imagery of bare-soil fields can be used to quantify and mapthe spatial variation of total phosphorus concentration in surfacesoils. This research has significant implications for identification andmapping of areas with high phosphorus, which is important forimplementing and monitoring the best phosphorus management practicesacross the region.

In addition to the features mentioned above, objects and advantages ofthe present invention will be readily apparent upon a reading of thefollowing description and through practice of the present invention.

Novel features and advantages of the present invention, in addition tothose mentioned above, will become apparent to those skilled in the artfrom a reading of the following detailed description in conjunction withthe accompanying drawings summarized as follows.

BRIEF DESCRIPTION OF THE DRAWINGS

The file of this patent contains at least one drawing executed in color.Copies of this patent with color drawing(s) will be provided by thePatent and Trademark Office upon request and payment of the necessaryfee.

FIG. 1 is a LANDSAT 5 TM natural color image (TM bands 1, 2, and 3displayed as BGR, respectively) obtained on May 20, 2005 showing theeastern part of Lucas County in northwest Ohio; this area drains intoLake Erie, which is towards the northern side (top) of the image, inaccordance with one embodiment of the present invention.

FIG. 2 is a graph showing averaged (n=35) spectral reflectance of thesoil samples collected at 0, 30 and 50 cm depths in F34 and F11 treatedfields, in accordance with one embodiment of the present invention.

FIG. 3 is a graph showing actual versus predicted phosphorusconcentration (in mg/kg) of surface soil samples using the dark objectsubtracted best phosphorus spectral ratio model being applied to theLANDSAT 5 TM frame of May 20, 2005, which was also used for developingthe model, in accordance with one embodiment of the present invention.

FIG. 4 is a graph showing actual versus predicted phosphorusconcentration (in mg/kg) of surface soil samples using the dark objectsubtracted best phosphorus spectral ratio model being applied to theLANDSAT 5 TM frame of Jun. 5, 2005, in accordance with one embodiment ofthe present invention.

FIG. 5 is an image showing the total phosphorus concentration (mg/kg) insurface soil samples of fields F34 (left side of the image) and F11(right side of the image), displayed as red (high phosphorus content) toturquoise (low phosphorus content), obtained by applying the bestphosphorus spectral ratio model to the LANDSAT 5 TM frame of May 20,2005 which was also used for developing the model, in accordance withone embodiment of the present invention.

FIG. 6 is an image showing the total phosphorus concentration (mg/kg) insurface soil samples of fields F34 (left side of the image) and F11(right side of the image), displayed as red (high phosphorus content) toturquoise (low phosphorus content), obtained by applying the phosphorusspectral ratio model to the LANDSAT 5 TM frame of Jun. 5, 2005, inaccordance with one embodiment of the present invention.

FIG. 7 is an image showing the total phosphorus concentration (mg/kg) insurface soil samples of the bare soil fields in the eastern part of theLucas County of northwest Ohio, which is a part of the drainage basin ofLake Erie, which is located at the northern side (top) of the image, inaccordance with one embodiment of the present invention.

FIG. 8 shows LANDSAT TM images of a fly ash spill area acquired andanalyzed in accordance with the one embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENT(S)

In accordance with the foregoing summary, the following is a detaileddescription of the preferred embodiments of the invention, which areconsidered to be the best mode thereof. The preferred method and systemherein described is not intended to be exhaustive or to limit theinvention to the precise forms disclosed. They are chosen and describedto explain the principles of the invention and the application of themethod to practical uses so that others skilled in the art may practicethe invention.

Application to the Remote Determination of Phosphorus

Materials and Methods

Soil Sampling and Chemical Analysis

Two adjacent agricultural fields, designated F34 and F11, that receiveda cumulative amount of 34 ton acre-1 (76 mg hac-1) and 11 ton acre-1 (25mg hac-1) of Class B biosolids on a dry weight basis during the periodof 1985-2002 were selected for this embodiment (See FIG. 1).

FIG. 1 shows the LANDSAT 5 TM natural color image (TM bands 1, 2, and 3displayed as BGR, respectively) obtained on May 20, 2005 showing theeastern part of Lucas County in northwest Ohio; this area drains intoLake Erie, which is towards the northern side (top) of the image. Thefields permitted for Class B biosolid application in the area areoutlined in the image. The fields marked with red borders are theexperimental fields used in this embodiment. Soil sampling locations ofthe study area were shown as yellow dots in the insert image.

Soil samples were collected at 0, 30, and 50 cm depths from each of the70 sampling locations across the two fields. These fields were selectedbecause they are representative of large areas of northwest Ohio whereland application of biosolids has become an important agriculturalpractice.

The soil samples were collected on May 19, 2005, one day prior toLANDSAT over pass, and the sampling locations were marked using aTrimble GeoExplorer (Trimble Navigation Limited, CA, USA) globalpositioning system (GPS) receiver. The collected soil samples were driedand passed through a 2 mm sieve. The moisture content of the surfacesoil samples was measured using the gravimetric method. The source ofsewage sludge for the agricultural fields in the study area was theOregon Waste Soil Treatment Plant (OWSTP). The basic composition of thesewage sludge of OWSTP is typical of the bio-solids produced in Ohio,which is regulated within the limits set by the U.S. EnvironmentalProtection Agency (USEPA) under the part 503 rule (USEPA, 2002).

Soil samples (approximately 0.5 g) were digested with concentrated HNO3,according to USEPA method SW846-3051A (USEPA, 1998) using a Mars Xpressmicrowave digestion unit (CEM, Matthews, N.C., USA). The digestedsolution was filtered and then analyzed for As, B, Be, Ca, Cd, Cr, Cu,Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, S, Se, Si and Zn concentrations usinginductively coupled plasma-optical emission spectrometry (ICP-OES) (IRISIntrepid II, Thermo Scientific, Waltham, Mass., USA). Quantification wasachieved using matrix matched high and low concentration standards foreach element. Internal quality controls and blanks were run every tensamples in order to quantify cross-contamination and recoveryefficiencies.

Analysis of variance (ANOVA) was used to compare the accumulation ofeach element in the F34 and F11 fields using SAS version 9.1 statisticalsoftware (SAS Institute Inc., Cary, N.C., USA). An alpha level of 0.05was used to determine the significance.

LANDSAT Data Acquisition and Analysis

The LANDSAT image frames of May 20 and Jun. 5, 2005, covering the studyarea were downloaded soon after soil sampling. The images were thenprocessed with the ER Mapper image processing software, a commercialproduct of Earth Resources Mapping, Inc. The study area was locatedwithin the LANDSAT overpass region of Path 20, Row 31. The natural colorimage of the study area, overlaid with outlines of the fields permittedfor Class B biosolid applications, is shown in FIG. 1. The locations ofall the 70 soil sampling points collected one day prior to LANDSAT 5overpass were also shown separately in FIG. 1 on the natural color imageof the study area. The study site was dry, without any vegetation, suchthat the image spectral reflectance represent the spectral reflectanceof the bare soil. The procedure for developing the GIS database of theClass B biosolid permitted fields in Wood and Lucas counties ofnorthwest Ohio was reported in detail by McNulty (2005).

Based on the locations of the 70 soil samples, the dark objectsubtracted (DOS) pixel values corresponding to the LANDSAT TM bands 1-5and 7 were derived from the original May 20, 2005 image. The spectralrange of these LANDSAT TM bands are as follows: band 1: 450-520 nm; band2: 520-600 nm; band 3: 630-690 nm; band 4: 760-900 nm; band 5: 1550-1750nm; and band 7: 2080-2350 nm.

The dark object of each spectral band is defined as one value less thanthe minimum digital number found in all the pixels of the image (Vincentet al., 2004) referenced herein below. The detailed procedure for DOSand its effects on removal of atmospheric haze was given in Vincent(1997) and Vincent et al. (2004) referenced herein below.

From the DOS-corrected digital number (DN) values of the six LANDSATsingle bands, 15 non-reciprocal spectral ratios were calculated. Thesespectral ratios are: R2,1; R3,1; R3,2; R4,1; R4,2; R4,3; R5,1; R5,2;R5,3; R5,4; R7,1; R7,2; R7,3; R7,4; R7,5 where R represents the ratioand the numbers represent the LANDSAT TM band numbers (Vincent, 1997).The spectral ratios were calculated using the MINITAB statisticalsoftware (MINITAB Inc., State College, Pa., USA).

LANDSAT TM Best Spectral Ratio Model Development and Validation

The relationships between the chemical concentrations of the surfacesoil samples and the DOS DN values corresponding to the six single bandsand the 15 non-reciprocal spectral ratios were developed by regressionanalysis. Using the MINITAB regression analysis component the bestsubsets regression was employed, and only the top two models with thehighest R2 adjusted values were chosen to report for each number ofvariables. The best subsets procedure was used for sequentially enteringindependent variables one at a time to improve the regression equation'spredictive ability. The reported models from the best subset regressionoutput were tested for autocorrelation with a Durbin-Watson (DW)statistical test (Durbin and Watson, 1951). This tests forautocorrelation in the input parameters. Finally, the model which hadthe highest R2 adjusted and that also passed the DW test was selected asthe best model for given inputs. This procedure was reported in detailelsewhere by Vincent (2000) and Vincent et al. (2004). The identifiedbest model was then applied to the same May 20, 2005, LANDSAT image,which was used in developing the model to map the elementalconcentration of the surface soils. The model was also applied andvalidated using the Jun. 5, 2005, LANDSAT image, which was obtained 17days after the soil sampling. In the LANDSAT images that were appliedwith the best model, masks were created to limit the display to onlybare soil fields.

Laboratory Spectral Data Acquisition and Analysis

A Fieldspec Pro spectroradiometer (ASDInc., Boulder, Colo., USA) with aspectral range of 350-2500 nm was used to obtain the reflectance spectraof the collected soil samples in the laboratory, with aquartz-tungsten-halogen (QTH) lamp as a light source. Diffused lightfrom the 100 W Lowell Pro-Light was used to illuminate the soil samplesthat were placed in a Petri plate at 45° angles, when spectra werecollected in the laboratory. The fore-optics of the spectroradiometerwas aligned vertically, and the height of the fore-optics was adjustedso that reflected light only from the surface of the soil samples filledthe field of view (FOV) of the instrument. The height of the fore-opticswas kept constant throughout the experiment at 20 cm from the surface ofeach soil sample. The same experimental setup was used to obtain thespectra of all the soil samples collected at 3 different depths fromeach field.

Calibration spectra of a white Spectralon panel (Labsphere Inc., NorthSutton, N.H.) were acquired before recording the soil spectra. Thespectral recording software in the spectroradiometer was set in such away that each reflectance spectrum recorded was obtained by collectingand averaging 20 individual reflectance spectra. Each spectrum wasnormalized by dividing it by the measured spectrum of the standard(Spectralon panel). The configuration of the ASD spectroradiometerconsists of three detectors, each collecting spectra from the 350-1050,900-1850, and 1700-2500 nm spectral regions, respectively. The spectracollected by these detectors within the instrument are not splicedtogether. Thus, each normalized spectrum was splice-corrected with theASD ViewSpec software (ASD Inc., Boulder, Colo.). Individual spectralmeasurements of the soil samples corresponding to the three samplingdepths in each of the fields were then averaged to overcome the spectralvariations.

Results

Soil Chemical Concentration

The chemical concentration of the soils at 0, 30 and 50 cm depths andthe moisture content of the surface soils in both the F34 and F11treated fields are shown in Table 1.

TABLE 1 Chemical concentration of soils applied with 34 ton acre⁻¹ (F34)and 11 ton acre⁻¹ (F11) of Class B Biosolids Soil Depth Ba Cd Cu S PMoisture Treatment (cm) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (%) F340 161 5.7 55 405 2550  9.7 (±28.5) (±0.5) (±5) (±57) (±625) (±7.3) 30103 5.7 37 197 796 (±16.7) (±0.46) (±5.1) (±55) (±439) 50  97 5.6 35 154557 (±14.4) (±1.1) (±6.1) (±31) (±103) F11 0  98 3.6 37 265 988 5.5(±19.1) (±1.9) (±6.6) (±68) (±303) (±2.5) 30 100 3.5 31 165 588 (±13.3)(±0.9) (±5.1) (±49) (±177) 50  99 3.7 31 132 558 (±13.5) (±0.9) (±4.9)(±51) (±149) The given values are means ± standard deviation of 35replicates

Among all the chemicals that were analyzed, the accumulation of Ba, Cd,Cu, S and P were significantly (pb0.05) higher in the surface soils ofF34, compared to F11 (Table 1). There was no significant difference inthe chemical concentrations at 30 and 50 cm depths among the F34 and F11soils. Also, the moisture content of the surface soils in both thefields was similar (Table 1). The soils were of the prevalent lattysilty clay type with surface soils having 40-55% of clay and 3-5% oforganic matter (Soil Survey Staff, 2007).

Spectral Reflectance of Soil Samples

The averaged spectral reflectance of the F34 and F11 soils obtained at0, 30 and 50 cm depths are shown in FIG. 2. Also given are the averagedtotal P concentrations (in mg/kg) corresponding to the soil samples. Thespectral reflectance of the soils decreases with increase in Pconcentration. The surface soil samples of field F34 have high Pconcentration (2550 mg/kg) and low spectral reflectance throughout thespectral range, compared to the rest of the soil samples. Thereflectance of the soil samples gradually increases from 350 nm to about2200 nm, then decreases to about 2500 nm. There is a broad, shallowreflectance minimum between 600 and 1100 nm (centered about 850 nm),which is likely due to trace amounts of iron present in the soils. Thetwo absorption bands (reflectance minima) near 1400 and 1900 nm in thespectra are due to the in-situ soil moisture. All the soil samples thatwere used in obtaining the spectra in the laboratory were dried andpassed through a 2 mm sieve to minimize the effects of soil moisture andparticle size on the spectra.

LANDSAT Spectral Ratio Model

Regression equations were established to determine the chemicalconcentrations of Ba, Cd, Cu, S and P, which are significantly (pb 0.05)higher in the surface soils of F34 compared to F11, using theDOS-corrected six TM bands and the 15 non-reciprocal spectral ratios.The best spectral ratio input models that pass the DW test ofsignificance along with their R2 adjusted and standard error values aregiven in Table 2.

TABLE 2 Best spectral ration input models for phosphorus, copper, andsulfur that pass the Durbin-Watson test along with the values of R²adjusted and SE (standard error) Best spectral ratio Chemical model R²adjusted (%) SE (mg/kg) Phosphorus 4156-1690 (R51) + 67.9 531.2 Copper2257 (R73) 59 6.9 Sulfur 75-17.9 (R51) + 21.9 49.3 66.8 (R73) 507-14.7(R51) + 214 (R73) Note: The models developed for Cd and Ba did not passthe Durbin-Watson test at 5% level of significance.

None of the single band models passed the DW test. Phosphorus had thehighest R2 adjusted value (67.9%) among the chemical attributes thatpassed the DW test (Table 2) and are considered for mapping phosphoruswith LANDSAT TM data. Hence, only the phosphorus results were shown inthis paper. The phosphorus values obtained from chemical analysis of the70 surface soil sampling locations versus the predicted values ofphosphorus for the same locations obtained by applying the phosphorusspectral ratio model P(mg/kg)=4156−1690 (R51)+2257 (R73) to the May 20,2005, LANDSAT TM frame is given in FIG. 3. The phosphorus spectral ratiomodel was also applied to the Jun. 5, 2005 LANDSAT image frame and thepredicted phosphorus values were plotted against the phosphorus valuesobtained by the soil analysis (FIG. 4). The model performed well inpredicting the phosphorus concentrations of surface soil when applied toeither of the LANDSAT TM images.

The application of the best phosphorus spectral ratio model to theLANDSAT 5 TM frame of May 20, 2005, which was also used in developingthe model, is shown in FIG. 5. The redder color in this imagecorresponds to higher amounts of phosphorus in surface soil. FIG. 6shows the image of the same spectral ratio model that was developedusing the LANDSAT 5 frame of May 20, 2005, being applied to the LANDSAT5 frame of Jun. 5, 2005. Note that the phosphorus concentration in theF34 field is significantly higher than the F11 field in both the images(FIGS. 5 and 6). The application of the best phosphorus spectral ratiomodel to the May 20, 2005, LANDSAT TM image, showing the part of thewatershed that drains into Lake Erie, is given in FIG. 7. The fieldsoutlined in this figure are permitted for Class B biosolid application.

Discussion

The analytical results showed that the accumulation of phosphorus insurface soil samples of F34 was about 2.6 times higher than for the F11soils. This confirms the report of Chang et al. (1983) that fivecontinuous years of biosolids application in two California soils at 0,22.5, 45 and 90 ton per hectare increased the total phosphorusconcentration of surface soil (0-15 cm) from 515-540 mg/kg to 1092-1312,1657-2163 and 2617-3470 mg/kg, respectively. Similarly Maguire et al.(2000) reported that the concentration of total soil phosphorus insurface soils (0-20 cm) of biosolid amended soils was 738 mg/kg, ornearly double the values in unamended soils, where the total soilphosphorus was 403 mg/kg. High concentrations of Cd and Cu in thesurface soils of F34 compared to F11 agree with the reports ofNyamangara and Mzezewa (1999), that the long-term application ofbiosolid increases the accumulation of Cd and Cu in the surface soils.

The spectral results showed that the intensity of spectral reflectance(from 350-2500 nm spectral range) decreases with increases in phosphorusconcentration of the soils (FIG. 2) agreeing with the results ofBogrekci and Lee (2005). Bogrekci and Lee (2005) also showed that theremoval of phosphorus and other nutrients from soils through leachingresults in an increase in the spectral reflectance of soils. In thisexample, the reflectance of the soil samples (FIG. 2) decreased more inthe NIR region compared to the visible region. Bogrekci and Lee (2007)found a good relationship between reflectance and phosphorusconcentration with coefficients of determination of 0.93, 0.95 and 0.76for total, Mehlich-1 and water soluble phosphorus. The reflectance ofF34 surface soil samples is low compared to the rest of the soil samplesand this can be attributed to its high total phosphorus concentration of2550 mg/kg (FIG. 2). LANDSAT TM data can be used to estimate and mapsome chemical characteristics of soils, such as total phosphate content,as shown in this example. Although not limited to the theory by whichthe invention operates, these results allow one to conclude thatremotely sensed imagery of bare soil fields can be used to quantify andmap the spatial variation of total phosphorus concentration in surfacesoils. The technology is simple enough to be applied to the entirewatershed. The phosphorus spectral ratio model was more robust andreliable than the single band input models and can be applied to baresoil fields with low (b13%) soil moisture.

Nanni and Dematte (2006) have successfully employed LANDSAT TM data toestimate the sand, silt, clay, organic matter, cation exchange capacity(CEC) and sum of cations in Brazilian soils. They derived spectralreflectance values from the corrected LANDSAT image to develop multipleregression equations in order to estimate the different physical andchemical characteristics of the soils; however, no soil maps werepresented in that study (Nanni and Dematte, 2006). The present inventionis believed to be significant because it represents the first successfuleffort in using LANDSAT TM data to estimate and map phosphorusconcentration in surface soils. The phosphorus spectral ratio model wasalso successfully validated by applying it to another LANDSAT imageobtained on Jun. 5, 2005.

Aerial imagery was used to map the organic carbon (Chen et al., 2000),clay content (Sullivan et al., 2005), organic matter and Bray-1phosphorus concentration (Varvel et al., 1999) and LANDSAT TM imagerywas used to estimate the physical and chemical properties (Nanni andDematte, 2006) of surface soils in the previous studies.

However, the algorithms developed in accordance with the preferredembodiment of the present invention were based on the reflected imageintensity values of the soils, which required correction for atmospherichaze with atmospheric models before applying the algorithms to anotherdate. The phosphorus spectral ratio model developed in this example isbased on the DOS-corrected spectral ratios and is more robust than anymodel that could be derived from a combination of single spectral bands.Vincent et al. (2004) showed that the DOS spectral ratio models weremore robust than single band models and can be applied with reasonableaccuracy to different times of data collection, though their subject wascyanobacteria blooms in lakes or streams, and the present example isabout phosphorus concentrations in bare soils on dry land.

By applying the phosphorus spectral ratio model, one can identify andmap the phosphorus concentration in surface soils as a result ofbiosolid application.

Because phosphorus accumulation in soils can also result from theapplication of biosolids, animal manures, and man-made fertilizers, thisresearch has significant implications in identifying the fields withhigh concentrations of surface soil phosphorus, thus helping in theimplementation of phosphorus-based management practices on agriculturalfields, with an aim toward reduction of phosphorus runoff into nearbysurface water bodies.

Shober and Sims (2003) reported that twenty-four of the states andterritories in the United States now have regulations to restrict theland application of biosolids, based on phosphorus concentration insoil.

Thirteen of these 24 states have established actual numerical limits forsoil test phosphorus (STP), with an aim to cease the application ofbiosolids once these limits are reached. As the total soil phosphorusand STP are linearly related to each other (Allen and Mallarino, 2006),the phosphorus spectral ratio model in accordance with the presentinvention can be used to monitor phosphorus levels in surface soils.

One limitation of this phosphorus model is that it was developed usingbare soil fields that had low surface soil moisture (b13%). While thereis currently no data respecting this model's performance on fields withsoil moisture contents greater than that value, one may apply thepresent invention based upon this phosphorus model on fields with highermoisture contents through making adjustments to accommodate thiscondition, without undue experimentation. One may also apply the presentinvention to determine the phosphorus concentration in surface soils ofother soil types in this region or other regions, though the soilstested were of the prevalent type (Latty Silty Clay).

Mapping and Estimation of Phosphorus and Copper Concentrations in FlyAsh Spill Area using LANDSAT TM Images Introduction

A vast amount of fly ash, a by-product of coal incineration, spilledover a wide area on Dec. 22, 2008, at approximately 0100 hours EST, whenan earthen wall of a fly ash disposal pond broke at the Tennessee ValleyAuthority (TVA) Kingston Fossil Plant, located at Harriman, RoaneCounty, Tenn. (TVA, 2009). This is the largest environmental disaster ofits kind involving a coal fly ash spill in US history (New York Times,2008). Approximately 5.4 million cubic yards of fly ash spilled over anarea of 300 acres outside the ash storage ponds. Fly ash also spilledinto the Emory River and the tributaries that flow into the Emory River,which serve as a source of drinking water (TVA, 2009; New York Times,2008).

Accumulation of large amounts of fly ash as a result of coal combustionis becoming a major environmental concern in the United States. With theNation's increasing demand for energy, the increase in coal combustionhas required the disposal of large quantities of coal combustionresidues.

Approximately 131 million tons of coal combustion residues weregenerated within the US in 2007, of which 36% was disposed in landfills,21% in surface impoundments, and the remainder was reused for beneficialpurposes. There are approximately 300 landfills and 300 surfaceimpoundment facilities used by 440 coal-fired plants across the US.

The physical and chemical properties of the fly ash generated at a givencoal-fired plant depends on the nature of the coal being burned, type ofthe combustion method, and the storage and handling methods involved. Ingeneral, fly ash is substantially rich in many elements (including heavymetals) and is usually stored in either landfills or artificial lagoonson open land. The general chemical composition of fly ash is given inTable 1 (Page et al., 1979). Mapping the spatial distribution ofchemical concentrations as a result of fly ash spill is important fordetermining its effect on human and environmental health and forconducting remediation and recovery efforts. Previous studies have shownthat fly ash pollution can be monitored through several methods:conventional soil sampling and analysis, measurement of Sr-87 to Sr-86ratios in plant samples growing in the vicinity, and measurement offerromagnetic mineral concentrations present within the fly ash bymagnetic mapping. However, all of these methods are based on pointmeasurements at the ground-level, requiring intensive sampling oflarge-scale contaminated areas, which is expensive, time-consuming andsometimes not even possible as the areas may be sufficiently hazardous(e.g., poor slope stability) and inaccessible to scientists andengineers.

LANDSAT TM Images

Remote sensing has been used as an alternative method for determiningand mapping the physical and chemical characteristics of exposed soils.In this embodiment, LANDSAT Thematic Mapper (TM) sensor data was used toidentify and map the TVA's fly ash spill in Tennessee.

LANDSAT TM is a medium-resolution, multi-spectral imager with spectralbands 1-7 covering the visible, near-infrared, and thermal regions ofthe electromagnetic spectrum. The spectral range of these LANDSAT TMbands are as follows: Band 1: 450-520 nm; Band 2: 520-600 nm; Band 3:630-690 nm; Band 4: 760-900 nm; Band 5: 1,550-1,750 nm; Band 6:10,400-12,500 nm and Band 7: 2,080-2,350 nm. The Thematic Mapper andEnhanced Thematic Mapper plus (ETM+) sensors were aboard the LANDSAT 5and 7 respectively, each of which has a 16-day repeat cycle with a pixelsize of 30 m×30 m for bands 1-5 and 7. Because the two satellites havean 8-day offset from one another, it is possible to obtain coverage of agiven ground area every 8 days, though the TM sensor aboard LANDSAT 7(called ETM+) has approximately a quarter of its pixels missing, due tothe loss of its scan line converter since May 31, 2003. In order tomonitor the before and after fly ash spill events of the Kingston flyash plant, LANDSAT 5 TM data acquired on Nov. 20, 2008 (32 days beforethe fly ash spill), Dec. 22, 2008 (about 9 hours after the fly ashspill), and Feb. 1, 2009 (40 days after the fly ash spill) wasdownloaded for this embodiment. The LANDSAT TM images were thenprocessed with the ER Mapper image processing software, a commercialproduct of Earth Resources Mapping, Inc.

Measuring the Chemical Concentrations from LANDSAT TM Imagery

The dark-object-subtracted (DOS) pixel values corresponding to LANDSATTM bands 1-5 and 7 were derived from each of the images. The dark objectof each spectral band is defined as one value less than the minimumdigital number found in all the pixels of the image for that spectralband. The objective of this example was to show that the phosphorus andcopper concentrations in the exposed fly ash and soil in the spillvicinity can be estimated and mapped using LANDSAT TM data. For thispurpose, the spectral ratio models that were developed to estimate andmap the phosphorus and copper concentrations in surface soil samples ofsewage sludge amended soils (Sridhar et al., 2009) were applied to eachof the three LANDSAT TM images covering the fly ash spill study area.The algorithms used for estimation of phosphorus and copperconcentrations are as follows: P (ppm)=4156−1690 (R51)+2257 (R73); Cu(ppm)=75−17.9 (R51)+21.9 (R73), where R51 and R73 are thedark-object-subtracted (haze corrected) values of TM band 5 divided byTM band 1 and TM band 7 divided by TM band 3, respectively. The R2(Adjusted) values and standard error for the P algorithm were 67.9% and531 ppm, respectively, and for the Cu algorithm were 59% and 6.9 ppm.For each of the three LANDSAT TM images to which these spectral ratiomodels were applied, masks were created to limit the display to onlybare soil fields. Details of these algorithmic models were reportedelsewhere (Sridhar et al., 2009).

Results of Image Analysis

FIG. 8 shows the natural color images, total phosphorus and copperconcentration images of the fly ash-spill vicinity for each of thefollowing dates of LANDSAT TM overpass: Nov. 20, 2008, Dec. 22, 2008,and Feb. 1, 2009, representing the periods of 32 days before the fly ashspill, 9 hours after, and 40 days after the fly ash spill, respectively.The images in rows 1-3 represent the natural color image, surface Pconcentration image, and surface Cu concentration image, respectively.

The fly ash is seen as a grey-colored area in the Dec. 22, 2008 naturalcolor imagery. Image interpretation of a standard natural color image(TM bands 1, 2, and 3 displayed as blue, green, and red, respectively)yields limited success for mapping the fly ash deposits because thecontrast between fly ash and background soils is small in visiblewavelength regions.

The LANDSAT TM derived P concentrations range from 1,500 to 4,500 ppmand the Cu concentrations from 25 to 75 ppm in the November, 2008 andFebruary, 2009 images.

Turquoise, yellow, orange and red colors were assigned to increasingphosphorus and copper concentrations as indicated by the respectivecolor coding bars in FIG. 8. The intense red color in the Dec. 22, 2008image clearly stands out due to larger areas of high phosphorus andcopper concentration exposures at the surface, compared to the Nov. 20,2008 image acquired before the fly ash spill. The 25 to 75 ppm range ofCu concentrations derived from the Dec. 22, 2008 image correlates wellwith the 29.9 to 69.4 ppm range of Cu values obtained through laboratoryanalysis from eight (8) ash sampling locations collected from Dec. 23,2008 through Jan. 5, 2009 in the fly ash spill vicinity (Tetra Tech,2009). The P concentrations were not reported through soil analysis(Tetra Tech, 2009).

In November, before the fly ash spill, the high concentrations ofphosphorus and copper were shown to be confined to the fly ash ponds. InDecember, a large extent of increased phosphorus and copperconcentrations is clearly evident toward the northern and northeasternside of the ash holding ponds as shown in FIG. 8. These high phosphorusand copper concentration features agree with the fly ash spill towardthe northern and northeastern sides of the ash holding ponds (TVA,2009). In February, the phosphorus and copper concentrations mapped withthe same LANDSAT TM algorithms were lower than those recorded inDecember. As an action item for dust suppression, TVA spread 85 tons ofwinter rye grass, 650 tons of straw and other erosion-control mulch onthe surface of the fly ash spill areas from Jan. 3 through Jan. 15, 2009(TVA, 2009).

For areas where grass and straw were spread over, the Feb. 1, 2009 imageshows lower phosphorus and copper concentrations, compared to the Dec.22, 2008 image. This series of images shows that LANDSAT TM data can beused to quantitatively monitor the remediation and recovery efforts ofphosphorus and copper contaminated sites.

CONCLUSIONS

In summary, the application of phosphorus and copper mapping algorithmsfrom LANDSAT TM images have allowed us to map and estimate the increasein surface concentrations of these two elements as a result of fly ashspill. These results show the effective use of multispectral satelliteimage analysis in determining surface soil chemical concentrations.

Compared to point measurements made by ground-based soil sampling andchemical analysis, the satellite-based measurements have a greatadvantage in mapping the spatial distribution and concentration ofelements and chemical compositions over time. As LANDSAT TM has 30-mspatial resolution, there is a measurement by the satellite for every ⅕of an acre (the area covered by one pixel) during each overpass, whichwould be cost prohibitive for point measurements that require manualsoil sample collection and laboratory analysis of each sample. Satellitemonitoring of surface soil elemental concentrations for environmentalpurposes can surpass point measurements on the ground and measured inthe laboratory, at least for phosphorus and copper.

The results obtained in this embodiment as regards phosphorus and copperwere found to be the best compared to several other elements that werestudied for similar algorithms (Sridhar et al., 2009). However, withmore and better suited spectral bands, future satellites hold thepromise of enabling us to move beyond the point-based in situmeasurements provided by soil sampling, except for checks on thesatellite algorithms that measure surface elemental and chemicalcompound concentrations from the spectral behavior of their reflectanceof sunlight and emittance of heat from the Earth's surface.

Additional background for the invention is provided by the followingreferences which are hereby incorporated by reference.

REFERENCES

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Having shown and described a preferred embodiment of the invention,those skilled in the art will realize that many variations andmodifications may be made to affect the described invention and still bewithin the scope of the claimed invention. Thus, many of the elementsindicated above may be altered or replaced by different elements whichwill provide the same result and fall within the spirit of the claimedinvention. It is the intention, therefore, to limit the invention onlyas indicated by the scope of the claims.

1. A method of measuring sulfur in soil from light reflected therefrom,said method comprising the steps of: (a) obtaining a measurement ofreflected light from said soil using a light measurement device, saidmeasurement comprising a measurement of the respective amount of lightin at least two wavelength ranges, (b) applying one or more correctionto the measurement of the respective amount of light in at least twowavelength ranges, wherein the correction applied is selected from thegroup consisting of: dark object subtraction, vegetation masking,additive sensor offset, and atmospheric haze; and (c) determining theapproximate amount of sulfur in said soil from said respective correctedamounts of light by applying an algorithm using a microprocessor torelate said respective corrected amounts of light in said at least twowavelength ranges to the amount of sulfur in said soil, wherein saidalgorithm comprises a ratio of the respective corrected amount of lightin said at least two wavelength ranges.
 2. A method according to claim 1wherein said at least two wavelength ranges comprises four wavelengthranges.
 3. A method according to claim 2 wherein said algorithmcomprises a ratio of the respective amount of light in a first pair ofsaid four wavelength ranges, and a ratio of the respective amount oflight in a second pair of said four wavelength ranges.
 4. A methodaccording to claim 1, wherein said method comprises the steps of: a)obtaining a measurement of reflected light from said soil using a lightmeasurement device, said measurement comprising a measurement of therespective amount of light in at least four wavelength ranges: (i) Band1 from about 0.45 μm to about 0.52 μm; (ii) Band 3 from about 0.63 μm toabout 0.69 μm; (iii) Band 5 from about 1.55 μm to about 1.75 μm; and(iv) Band 7 from about 2.08 μm to about 2.35 μm, (b) applying one ormore correction to the measurement of the respective amount of light inat least four wavelength ranges, wherein the correction applied isselected from the group consisting of: dark object subtraction,vegetation masking, additive sensor offset, and atmospheric haze; and(c) determining the approximate amount of sulfur in said soil from saidrespective corrected amounts of light by applying an algorithm using amicroprocessor to relate said respective corrected amounts of light insaid at least four wavelength ranges to the amount of sulfur in saidsoil, wherein said algorithm comprises a ratio of the respectivecorrected amount of light in said at least four wavelength ranges.
 5. Amethod according to claim 4 wherein said algorithm is selected from thegroup consisting of S (mg/kg)=K₁−K₂ (R51)+K₃ (R73) wherein S is theamount of sulfur expressed in milligrams per kilogram; R51 is a ratio ofthe amount of reflected light in Band 5 to the amount of reflected lightin Band 1; and R73 is a ratio of the amount of reflected light in Band 7to the amount of reflected light in Band 3, and mathematical equivalentsthereof.
 6. A method according to claim 5 wherein: K₁ is a value in therange of from about 450 to about 550; K₂ is a value in the range of fromabout 13 to about 17; K₃ is a value in the range of from about 210 toabout
 220. 7. A method according to claim 6 wherein: K₁ is a value inthe range of from about 480 to about 530; K₂ is a value in the range offrom about 14 to about 16; K₃ is a value in the range of from about 212to about
 216. 8. A method according to claim 5 wherein: K₁ is a value ofabout 507±3; K₂ is a value of about 14.7±3; K₃ is a value of about214±3.
 9. A method according to claim 1 additionally comprising the stepof transmitting data relating to the approximate amount of said sulfurto a site remote from the site where said measurement takes place.
 10. Amethod according to claim 1 additionally comprising the step ofgenerating a report of said approximate amount of said sulfur in saidsoil.
 11. A system for measuring sulfur in soil from light reflectedtherefrom, said system comprising: (a) a measurement device adapted tomeasure reflected light from said soil, said measurement device adaptedto measure the respective amount of light in at least two wavelengthranges; and (b) a microprocessor capable of (i) applying one or morecorrection to the said measured respective amount of light in at leasttwo wavelength ranges, wherein the correction applied is selected fromthe group consisting of: dark object subtraction, vegetation masking,additive sensor offset, and atmospheric haze, and (ii) relating theapproximate amount of said sulfur in said soil to said respectivecorrected amounts of light by applying an algorithm relating saidrespective corrected amounts of light in said at least two wavelengthranges to the amount of sulfur in said soil, wherein said algorithmcomprises a ratio of the respective corrected amount of light in said atleast two wavelength ranges.
 12. A system according to claim 11 whereinsaid at least two wavelength ranges comprises four wavelength ranges.13. A system according to claim 12 wherein said algorithm comprises aratio of the respective amount of light in a first pair of said fourwavelength ranges, and a ratio of the respective amount of light in asecond pair of said four wavelength ranges.
 14. A system according toclaim 11, said system comprising: (a) a measurement device adapted tomeasure reflected light from said soil, said measurement device adaptedto measure the respective amount of light in at least four wavelengthranges: (i) Band 1 from about 0.45 μm to about 0.52 μm; (ii) Band 3 fromabout 0.63 μm to about 0.69 μm; (iii) Band 5 from about 1.55 μm to about1.75 μm; and (iv) Band 7 from about 2.08 μm to about 2.35 μm; and (b) amicroprocessor capable of (i) applying one or more correction to thesaid measured respective amount of light in at least four wavelengthranges, wherein the correction applied is selected from the groupconsisting of: dark object subtraction, vegetation masking, additivesensor offset, and atmospheric haze, and (ii) relating the approximateamount of said sulfur in said soil to said respective corrected amountsof light by applying an algorithm relating said respective correctedamounts of light in said at least four wavelength ranges to the amountof sulfur in said soil, wherein said algorithm comprises a ratio of therespective corrected amount of light in said at least four wavelengthranges.
 15. A system according to claim 14 wherein said algorithm isselected from the group consisting of S (mg/kg)=K₁−K₂ (R51)+K₃ (R73)wherein S is the amount of sulfur expressed in milligrams per kilogram;R51 is a ratio of the amount of reflected light in Band 5 to the amountof reflected light in Band 1; and R73 is a ratio of the amount ofreflected light in Band 7 to the amount of reflected light in Band 3,and mathematical equivalents thereof.
 16. A system according to claim 15wherein: K₁ is a value of about 507±3; K₂ is a value of about 14.7±3; K₃is a value of about 214±3.
 17. A system according to claim 11additionally comprising a transmitter adapted to transmit data relatingto the approximate amount of said sulfur in said soil from saidmicroprocessor to a site remote from the site where said measurementtakes place.
 18. A system for measuring sulfur in soil from lightreflected therefrom, said system comprising: (a) a satellite comprisinga measurement device adapted to measure reflected light from said soil,said measurement device adapted to measure the respective amount oflight in at least two wavelength ranges; and (b) a microprocessor indata communication with said satellite and capable of (i) applying oneor more correction to the said measured respective amount of light in atleast two wavelength ranges, wherein the correction applied is selectedfrom the group consisting of: dark object subtraction, vegetationmasking, additive sensor offset, and atmospheric haze, and (ii)determining the approximate amount of said sulfur in said soil to saidrespective corrected amounts of light by applying an algorithm relatingsaid respective corrected amounts of light in said at least twowavelength ranges to the amount of sulfur in said soil, wherein saidalgorithm comprises a ratio of the respective corrected amount of lightin said at least two wavelength ranges.